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In recent years under the trend of globalization, how a locality with its cultural and institutional vantages managed to articulate global economies as a node of ¡¥global city¡¦ is considerably concerned in the academic studies. With the framework of global city, this study aims to examine the transformation of role and space to the Taiwan animation industry. This study examines the historical development of the Taiwan animation industry to map out the trajectories of local strength, technological evolution, and the policies led by the government. The examination also shows that the dynamics of the animation industry has put Taiwan as a spatial mediator of global cities by the spatial clustering. In the new technological paradigm and the marketing strength, this study argues that the Taiwan animation industry transforms organization structure and accelerates itself to move to the metropolitan node of global cities. The transformations pilot the appearance of flexible workers, and the dominant firms that master decisive technologies have risen in these kinds of circumstances. However, the Taiwan animation firms those moved abroad do not shrank the industry, instead of using the production network to benefit comparative technologies and talents to distribute the systematized assignments over the global production network in the nodes of latecoming places, i.e. China and South-East Asia. And the Taiwan ¡¥node¡¦ tries to connect the pre-production network of American, European and Japan to reach the opportunities of co-production by strategic alliance, sharing mutual technologies, and global presence of the domestic and international film festivals. Via these networked multi-geometries to articulate the global economies as a node of global cities, the metropolitan region of Taipei permeates into the global production networks and improves the adaptability of the hierarchy of the global city.

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[Formulae and special characters can only be approximated here. Please see the pdf version of the abstract for an accurate reproduction.] This thesis is a new proposal for analysing spatial point patterns in spatial statistics using the outputs of popular techniques of (classical, non-spatial, multivariate) cluster analysis. The outputs of a chosen hierarchical algorithm, named fusion distances, are applied to investigate important spatial characteristics of a given point pattern. The fusion distances may be regarded as a missing link between the fields of spatial statistics and multivariate cluster analysis. Up to now, these two fields have remained rather separate because of fundamental differences in approach. It is shown that fusion distances are very good at discriminating different types of spatial point patterns. A detailed study on the power of the Monte Carlo test under the null hypothesis of Complete Spatial Randomness (the benchmark of spatial statistics) against chosen alternative models is also conducted. For instance, the test (based on the fusion distance) is very powerful for some arbitrary values of the parameters of the alternative. A new general approach is developed for analysing a given point pattern using several graphical techniques for exploratory data analysis and inference. The new strategy is applied to univariate and multivariate point patterns. A new extension of a popular strategy in spatial statistics, named the analysis of the local configuration, is also developed. This new extension uses the fusion distances, and analyses a localised neighbourhood of a given point of the point pattern. New spatial summary function and statistics, named the fusion distance function H(t), area statistic A, statistic S, and spatial Rg index, are introduced, and proven to be useful tools for identifying relevant features of spatial point patterns. In conclusion, the new methodology using the outputs of hierarchical clustering algorithms can be considered as an essential complement to the existing approaches in spatial statistics literature.

Understanding spatial perspectives on the spread and incidence of a disease is invaluable for public health planning and intervention. Choropleth maps are commonly used to provide an abstraction of disease risk across geographic space. These maps are derived from aggregated population counts that are known to be affected by the small numbers problem. Kernel density estimation methods account for this problem by producing risk estimates that are based on aggregations of approximately equal population sizes. However, the process of aggregation often combines data from areas with non-uniform spatial and population characteristics. This thesis presents a new method to aggregate space in ways that are sensitive to their underlying risk factors. Such maps will enable better public health practice and intervention by enhancing our ability to understand the spatial processes that result in disparate health outcomes.

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ABSTRACT AN ATTEMPT TO CLASSIFY TURKISH DISTRICT DATA: K-MEANS AND SELF-ORGANIZING MAP (SOM) ALGORITHMS Aksoy, Ece M.S., Department of Geodetic and Geographic Information Systems Supervisor: Assoc. Prof. Dr. Oguz ISik December 2004, 112 pages There is no universally applicable clustering technique in discovering the variety of structures display in data sets. Also, a single algorithm or approach is not adequate to solve every clustering problem. There are many methods available, the criteria used differ and hence different classifications may be obtained for the same data. While larger and larger amounts of data are collected and stored in databases, there is increasing the need for efficient and effective analysis methods. Grouping or classification of measurements is the key element in these data analysis procedures. There are lots of non-spatial clustering techniques in various areas. However, spatial clustering techniques and software are not so common. This thesis is an attempt to classify Turkish district data with the help of two clustering algorithms: K-means clustering and self organizing maps (SOM). With the help of these two common techniques it is expected that a clustering can be reached, which can be used for different aims such as regional politics, constructing statistical integrity or analyzing distribution of funds, for same data in GIS environment and putting forward the facilitative usage of GIS in regional and statistical studies. All districts of Turkey, which is 923 units, were chosen as an application area in this thesis. Some limitations such as population were specified for clustering of Turkey&rsquo / s districts. Firstly, different clustering techniques for spatial classification were researched. K-Means and SOM algorithms were chosen to compare different methods with Turkey&rsquo / s district data. Afterward, database of Turkey&rsquo / s statistical datum was formed and analyzed joining with geographical data in the GIS environment. Different clustering software, ArcGIS, CrimeStat and Matlab, were applied according to conclusion of clustering techniques research. Self Organizing Maps (SOM) algorithm, which is the best and most common spatial clustering algorithm in recent years, and CrimeStat K-Means clustering were used in this thesis as clustering methods.

Submitted by Guilherme Priólli Daniel (gui.computacao@yahoo.com.br) on 2016-09-06T13:30:29Z No. of bitstreams: 1 Dissertação_final.pdf: 2456534 bytes, checksum: 4d2279141f7c034de1e4e4e261805db8 (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-09-09T17:54:56Z (GMT) No. of bitstreams: 1 daniel_gp_me_sjrp.pdf: 2456534 bytes, checksum: 4d2279141f7c034de1e4e4e261805db8 (MD5) / Made available in DSpace on 2016-09-09T17:54:56Z (GMT). No. of bitstreams: 1 daniel_gp_me_sjrp.pdf: 2456534 bytes, checksum: 4d2279141f7c034de1e4e4e261805db8 (MD5) Previous issue date: 2016-08-12 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A quantidade de dados gerenciados por serviços Web de grande escala tem crescido significantemente e passaram a ser chamados de Big Data. Esses conjuntos de dados podem ser definidos como um grande volume de dados complexos provenientes de múltiplas fontes que ultrapassam a capacidade de armazenamento e processamento dos computadores atuais. Dentro desses conjuntos, estima-se que 80% dos dados possuem associação com alguma posição espacial. Os dados espaciais são mais complexos e demandam mais tempo de processamento que os dados alfanuméricos. Nesse sentido, as técnicas de MapReduce e sua implementação têm sido utilizadas a fim de retornar resultados em tempo hábil com a paralelização dos algoritmos de prospecção de dados. Portanto, o presente trabalho propõe dois algoritmos de agrupamento espacial baseado em densidade: o VDBSCAN-MR e o OVDBSCAN-MR. Ambos os algoritmos utilizam técnicas de processamento distribuído e escalável baseadas no modelo de programação MapReduce com intuito de otimizar o desempenho e permitir a análise em conjuntos Big Data. Por meio dos experimentos realizados foi possível verificar que os algoritmos desenvolvidos apresentaram melhor qualidade nos agrupamentos encontrados em comparação com os algoritmos tomados como base. Além disso, o VDBSCAN-MR obteve um melhor desempenho que o algoritmo sequencial e suportou a aplicação em grandes conjuntos de dados espaciais. / The amount of data managed by large-scale Web services has increased significantly and it arise to the status of Big Data. These data sets can be defined as a large volume of complex data from multiple data sources exceeding the storage and processing capacity of current computers. In such data sets, about 80% of the data is associated with some spatial position. Spatial data is even more complex and require more processing time than what would be required for alphanumeric data. In that sense, MapReduce techniques and their implementation have returned results timely with parallelization of data mining algorithms and could apply for Big Data sets. Therefore, this work develops two density-based spatial clustering algorithms: VDBSCAN-MR and OVDBSCAN-MR. Both algorithms use distributed and scalable processing techniques based on the MapReduce programming model in order to optimize performance and enable Big Data analysis. Throughout experimentation, we observed that the developed algorithms have better quality clusters compared to the base algorithms. Furthermore, VDBSCAN-MR achieved a better performance than the original sequential algorithm and it supported the application on large spatial data sets.

L’étude des phénomènes de regroupement spatial des individus en milieu urbain se focalisesouvent sur les espaces les plus fortement polarisés, qu’il s’agisse des « ghettos » de « riches » ou de« pauvres ». Pourtant, ces quartiers ne représentent qu’une partie très congrue de l’espace urbainfrançais. Sans occulter l’existence de ces zones, ce travail se propose d’étudier les phénomènes deregroupement spatial sous un angle exhaustif. Ceci a nécessité le développement d’une méthodologiepermettant de rendre compte de la complexité de la spécialisation socioéconomique etsociodémographique des territoires. L’impact de l’échelle géographique retenue dans les analyses surles résultats aussi bien en terme d’intensité que d’évolution est ainsi apparu comme une donnéecentrale de la problématique. En se basant sur cette analyse comparative des échelles géographiquesde regroupement, ce travail dresse un panorama des profils de regroupements des individus dans 18aires urbaines françaises. Il est ainsi apparu qu’il existe une grande similarité de ces phénomènes entreles grandes villes. Les individus s’y distribuent selon un modèle sectoriel d’un point de vuesocioéconomique quand ils suivent un schéma concentrique d’un point de vue sociodémographique.La combinaison de ces deux logiques a ainsi permis une analyse globale des phénomènes deregroupement socio-spatial. / The study of spatial clustering’s phenomena of people in zones often focuses on the moststrongly polarized spaces, it can be about "ghettos" of "rich" or "poor people". Nevertheless, theseareas represent a small part of the French urban space. Without denying the existence of these zones,this work will study the phenomena of spatial clustering from an exhaustive point of view. Thisrequired the development of a methodology allowing to take into account the complexity of thesocioeconomic and sociodemographic specialization of territories. The impact of the geographicalscale selected in analyses, on the results both in term of intensity and evolution is turned to be acentral topic of the problematic. By basing itself on this comparative analysis of the geographicalscales of clustering, this work gives an overview of the clustering profiles in 18 French urban areas. Itappeared that there is a similarity of these phenomena between big cities. Human distributions followa sectorial model on a socioeconomic point of view when they follow a concentric model bysociodemographic variables. The combination of these two paradigms of grouping so allowed a globalanalysis of the phenomena of socio-spatial clustering.